library(tidyverse) # for data cleaning and plotting
library(lubridate) # for date manipulation
library(openintro) # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps) # for map data
library(ggmap) # for mapping points on maps
library(gplots) # for col2hex() function
library(RColorBrewer) # for color palettes
library(sf) # for working with spatial data
library(leaflet) # for highly customizable mapping
library(carData) # for Minneapolis police stops data
library(ggthemes) # for more themes (including theme_map())
theme_set(theme_minimal())
# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")
starbucks_us_by_state <- Starbucks %>%
filter(Country == "US") %>%
count(`State/Province`) %>%
mutate(state_name = str_to_lower(abbr2state(`State/Province`)))
# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
place = c("Home", "Macalester College", "Adams Spanish Immersion",
"Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
"Dance Spectrum", "Pizza Luce", "Brunson's"),
long = c(-93.1405743, -93.1712321, -93.1451796,
-93.1650563, -93.1542883, -93.1696608,
-93.1393172, -93.1524256, -93.0753863),
lat = c(44.950576, 44.9378965, 44.9237914,
44.9654609, 44.9295072, 44.9436813,
44.9399922, 44.9468848, 44.9700727)
)
#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")
If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
ggmap)Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?world<-get_stamenmap(
bbox = c(left=-180, bottom=-57, right=179, top=82.1),
maptype = "terrain",
zoom=2)
ggmap(world)+
geom_point(data=Starbucks,
aes(x=Longitude, y=Latitude, color=`Ownership Type`))
theme_map()
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According to this graph, most of the Starbucks worldwide are licensed. US and East Asia have the highest amount of Company Owned location. The Franchises seem to be concentrated in Europe, and the Joint Venture locations seem to be in either Europe or East Asia.
twincities<-get_stamenmap(
bbox = c(left=-93.3538, bottom=44.8150, right=-92.9933, top=45.1210),
maptype = "terrain",
zoom=11)
ggmap(twincities)+
geom_point(data = Starbucks,
aes(x=Longitude, y=Latitude),color="purple")
theme_map()
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If I increase the zoom number, it zooms in further towards a street view and when I decrease the zoom number it zooms out too far and is blurry.
get_stamenmap() in help and look at maptype). Include a map with one of the other map types.twincities<-get_stamenmap(
bbox = c(left=-93.3538, bottom=44.8150, right=-92.9933, top=45.1210),
maptype = "watercolor",
zoom=11)
ggmap(twincities)+
geom_point(data = Starbucks,
aes(x=Longitude, y=Latitude))
theme_map()
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annotate() function (see ggplot2 cheatsheet).twincities<-get_stamenmap(
bbox = c(left=-93.3538, bottom=44.8150, right=-92.9933, top=45.1210),
maptype = "terrain",
zoom=11)
ggmap(twincities)+
geom_point(data = Starbucks,
aes(x=Longitude, y=Latitude))+
annotate(geom="text", x=-93.1712321, y=44.93790, label= "Macalester College", color="purple")
theme_map()
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geom_map())The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.
census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>%
separate(state, into = c("dot","state"), extra = "merge") %>%
select(-dot) %>%
mutate(state = str_to_lower(state))
starbucks_with_2018_pop_est <-
starbucks_us_by_state %>%
left_join(census_pop_est_2018,
by = c("state_name" = "state")) %>%
mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)
dplyr review: Look through the code above and describe what each line of code does.Line 172 reads in the data. Line 173 separates the state variable into the state name and the “.”, and I am not exactly sure what extra=merge means. In 174 the dot is not selected. In 175 the state variable is mutated to be in all lowercase letters. 177 names the new data set that has been created. 178 reads in the data, 179 joins the starbucks_us_by_state data to the census_pop_est_2018 dataset by the variable state which had to be recognized that state name was the same as state because in the starbucks data it is labeled as state_name. In line 181 a new variable is created that shows the number of starbucks per 100000 people.
states_map <- map_data("state")
starbucks_with_2018_pop_est %>%
ggplot()+
geom_map(map = states_map,
aes(map_id=state_name,
fill= starbucks_per_10000))+
scale_fill_gradient2() +
geom_point(data=Starbucks %>%
filter(Country=="US",!(`State/Province` %in% c("AK", "HI"))),aes(x=Longitude,
y=Latitude))+
labs(title = "Starbucks locations per 10,000 people",
caption= "Siona Kelly")+
theme_map()
From this map, I can conclude that most of the Starbucks locations in the United States are concentrated on the coasts, as well as bigger cities where there is more population.
leaflet)Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.
Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.
Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).
If there are other variables you want to add that could enhance your plot, do that now.
favorite_places_siona <- tibble(
place = c("Convention Grill", "Macalester College", "Target",
"Van Cleve Park", "Caibou Coffee", "Runyons",
"Trader Joes", "SEA LIFE", "Chuck and Dons"),
long = c(-93.329556, -93.1712321, -93.228456,
-93.231873, -93.166896, -93.270921,
-93.257392, -93.239559, -93.318756),
lat = c(44.921674, 44.9378965, 45.004879,
44.987869, 44.939563, 44.983274,
44.977127, 44.855135, 44.879186),
top3=c(TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE)
)
pal <-
colorFactor("viridis", domain = favorite_places_siona$top3)
leaflet(data=favorite_places_siona %>%
arrange(long)) %>%
addTiles() %>%
addProviderTiles(providers$Stamen.TerrainLabels) %>%
addCircles(lng= ~long,
lat= ~lat,
label = ~place,
color=~pal(top3),
weight = 15) %>%
addPolylines(lng= ~long,
lat= ~lat,
label = ~place,
color = "white") %>%
addLegend(pal = pal,
values=~top3)
This section will revisit some datasets we have used previously and bring in a mapping component.
The data come from Washington, DC and cover the last quarter of 2014.
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.TripStationData<-Trips %>%
group_by(sstation) %>%
summarize(departures=n(),
name=sstation) %>%
left_join(Stations,
by=c("name")) %>%
distinct(sstation, .keep_all = TRUE)
washingtondc<-get_stamenmap(
bbox = c(left=-77.0928, bottom=38.7930, right=-76.9383, top=38.9614),
maptype = "terrain",
zoom=12)
ggmap(washingtondc)+
geom_point(data = TripStationData,
aes(x=long, y=lat, color=TripStationData$departures))+
labs(title = "Washington DC Train Departures",
color="Total Departures")+
theme_map()+
theme(legend.background = element_blank())
CasualData<-Trips %>%
mutate(casual=ifelse(client == "Casual",1,0)) %>%
group_by(sstation) %>%
summarize(departures=n(),
name=sstation,
total_cas=sum(casual),
percent_cas=total_cas/departures*100) %>%
left_join(Stations,
by=c("name")) %>%
distinct(sstation, .keep_all=TRUE)
ggmap(washingtondc)+
geom_point(data=CasualData,
aes(x=long, y=lat, color=percent_cas))+
labs(title= "Casual Washington DC Train Departures",
y="",
x="")
It seems that there are a higher percent of casual users in the center of the city, around latitudes of 38.90 and longitudes of -77.05.
The following exercises will use the COVID-19 data from the NYT.
states_map <- map_data("state")
covid19 %>%
group_by(state) %>%
summarise(recent_count=max(cases)) %>%
mutate(state=str_to_lower(state)) %>%
ggplot() +
geom_map(map = states_map,
aes(map_id = state,
fill = recent_count))+
expand_limits(x = states_map$long, y = states_map$lat)+
theme_map()
The problem with this map is that it doesn’t take into account the populations of each state.
states_map <- map_data("state")
covid19 %>%
group_by(state) %>%
summarise(recent_count=max(cases)) %>%
mutate(state=str_to_lower(state)) %>%
left_join(census_pop_est_2018,
by=c("state")) %>%
mutate(cases_per_10000=(recent_count/est_pop_2018)*10000) %>%
ggplot()+
geom_map(map = states_map,
aes(map_id=state,
fill= cases_per_10000))+
scale_fill_gradient2()+
expand_limits(x = states_map$long, y = states_map$lat)+
theme_map()
These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.
MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.mpls_suspicious <-MplsStops %>%
group_by(neighborhood) %>%
summarise(stops_per_neighborhood=n(),
sus_stops_prop=sum(problem=="suspicious")/stops_per_neighborhood) %>%
arrange(desc(stops_per_neighborhood))
mpls_suspicious
leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.pal<-
colorFactor("viridis", domain=MplsStops$problem)
leaflet(data=MplsStops) %>%
addTiles() %>%
addProviderTiles(providers$Stamen.TerrainLabels) %>%
addCircleMarkers(lng= ~long,
lat= ~lat,
label = ~neighborhood,
color=~pal(problem),
stroke=FALSE,
radius=2) %>%
addLegend(pal = pal,
values=~problem)
eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)
## Error: Cannot open "Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp"; The file doesn't seem to exist.
mpls_all<-mpls_nbhd %>%
left_join(MplsDemo,
by=c("BDNAME"="neighborhood")) %>%
left_join(mpls_suspicious,
by=c("BDNAME"="neighborhood"))
## Error in left_join(., MplsDemo, by = c(BDNAME = "neighborhood")): object 'mpls_nbhd' not found
leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.pal<-
colorNumeric("viridis", domain=mpls_all$sus_stops_prop)
## Error in colorNumeric("viridis", domain = mpls_all$sus_stops_prop): object 'mpls_all' not found
leaflet(data = mpls_all) %>%
addTiles() %>%
addPolygons(fillColor = ~pal(sus_stops_prop),
stroke = FALSE,
label = ~paste(str_to_title(BDNAME))) %>%
addLegend(pal = pal,
values=~sus_stops_prop,
title = ~"Proportion of Stops",
position = "bottomright")
## Error in structure(list(options = options), leafletData = data): object 'mpls_all' not found
It seems as though there are less stops in the wealthier areas of the city.
leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.How does the proportion of college graduates vary based on neighborhood?
pal<-
colorNumeric("viridis", domain=mpls_all$collegeGrad)
## Error in colorNumeric("viridis", domain = mpls_all$collegeGrad): object 'mpls_all' not found
leaflet(data = mpls_all) %>%
addTiles() %>%
addPolygons(fillColor = ~pal(collegeGrad),
stroke = FALSE) %>%
#label = ~paste(str_to_title(BDNAME))) %>%
addLegend(pal = pal,
values=~collegeGrad,
title = ~"College Grads",
position = "bottomright")
## Error in structure(list(options = options), leafletData = data): object 'mpls_all' not found
DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?